Wind Resource Mapping Using Landscape Roughness and Spatial Interpolation Methods

Energy saving, reduction of greenhouse gasses and increased use of renewables are key policies to achieve the European 2020 targets. In particular, distributed renewable energy sources, integrated with spatial planning, require novel methods to optimise supply and demand. In contrast with large scale wind turbines, small and medium wind turbines (SMWTs) have a less extensive impact on the use of space and the power system, nevertheless, a significant spatial footprint is still present and the need for good spatial planning is a necessity. To optimise the location of SMWTs, detailed knowledge of the spatial distribution of the average wind speed is essential, hence, in this article, wind measurements and roughness maps were used to create a reliable annual mean wind speed map of Flanders at 10 m above the Earth’s surface. Via roughness transformation, the surface wind speed measurements were converted into meso- and macroscale wind data. The data were further processed by using seven different spatial interpolation methods in order to develop regional wind resource maps. Based on statistical analysis, it was found that the transformation into mesoscale wind, in combination with Simple Kriging, was the most adequate method to create reliable maps for decision-making on optimal production sites for SMWTs in Flanders (Belgium)

Assessment of full carbon budget of Italy: the CarbIUS project

Regional carbon balances, funded, for the Italian side, by the Italian Ministry of Environment in the context of a bilateral agreement to develop scientific collaborations in Global Change Research between Italy and USA signed in 2001. The two regions selected are Italy and Oregon-California; there are many similarities between these two regions (climate, vegetation, topography, population pressure, etc.) but, on other hand, there are also interesting contrasts in societal aspects like demography, land-use history and emissions. The main CarbIUS objectives are 1) the identification of spatial and temporal variability of carbon sources and sinks and the relative contribution of the different anthropogenic and biogenic components, 2) the impact of land use changes and human population dynamics on the carbon balance, 3) the quantification of the effects of climate and natural disturbances on the terrestrial carbon stocks and fluxes and 4) the application of new methodologies to investigate carbon metabolism at the plot, ecosystem and regional scale. In this paper will be presented the methodologies that we are using to assess the contribution of the different components to the full carbon budget, like carbon stocks and fluxes, disturbances (harvesting, wild forest fires and forest pathology), CH4 and NO2 fluxes and anthropogenic emissions. All these information will be input in a Data Assimilation System and the results will be validated using sub-regional airborne measurements of carbon fluxes

MANAGING CO2 EMISSIONS REGIONALLY USING GEOGRAPHICAL INFORMATION SYSTEM (GIS) SPATIAL MODELING AND PINCH ANALYSIS

Climate change has become the major global challenge of sustainability; among various anthropogenic sources of carbon dioxide (CO2) emissions, the burning of fossil fuels for energy to support commercial, residential, municipal and industrial sectors is considered to be the primary cause of increasing levels of carbon dioxide emissions. However, because climate change is regionally driven with global consequences, to analyze emissions data, energy planning techniques must be developed which are simple, replicable and optimized for maximum benefit. Climate scenarios are continually derived from global models despite these models containing little to no regional or local specificity. Place-based research, well grounded in local experience, offers a more tractable alternative for defining complex interactions among the environmental, economic, and social processes that drive greenhouse gas emissions. The focus of this study involves the development of a balanced energy supply and demand model under carbon constraints for the Southern Illinois energy sector; this sector represents the local specificity desired to build a carbon emissions pinch analysis model at the local level. This project is intended to formulate a robust methodology for constructing a Geographic Data Base Management System by employing a bottom/up approach to CO2 emissions modeling; the resulting data base can serve as the foundation for an environmental applications model employing pinch analysis techniques to address the allocation of energy resources and technologies to reduce CO2 emissions

Validating full scale metland solutions for decentralized sustainable wastewater treatment: techno-environmental and geospatial analysis

In recent decades increasing pressures on natural resources has drastically altered demographic dynamics and climate change. Currently, different lines of action are being pursued for the sustainable management and conservation of global water resources. In the field of wastewater treatment, the problem lies in small population centers where the scarcity of technical and economic resources compromises the effectiveness of conventional treatment methods. METland® technology emerges from the integration of Microbial Electrochemical Technologies (METs) into constructed wetlands. Integration improves treatment efficiency by replacing an inert material (gravel) with a biocompatible and electro-conductive material (ec-biochar or coke). Such designs maximize the transfer of electrons between ec-materials and electroactive bacteria. This makes full-scale METlands® a valid, sustainable, efficient, and robust wastewater treatment solution, with low operation and maintenance costs, for small and remote population centers. In this thesis, new strategies have been explored to improve the design and operation of full-scale METland® systems. A Life Cycle Analysis (LCA) was performed, evaluating the impacts of different operational modes on each environmental category. To explore the geospatial application of METlands, a process to evaluate optimal locations for their implementation was developed. The proposed methodology can be used to help decision-makers employ METland® worldwide using multi-criteria evaluation (MCE) techniques applied to Geographic Information Systems (GIS) with a final sensitivity analysis (SA) to optimize and validate the model

Mapping forest canopy fuel parameters at European scale using spaceborne LiDAR and satellite data

Spatially explicit data on forest canopy fuel parameters provide critical information for wildfire propagation modelling, emission estimations and risk assessment. LiDAR observations enable accurate retrieval of the vertical structure of vegetation, which makes them an excellent alternative for characterising forest fuel structures. In most cases, fuel parameterisation has been based on Airborne Laser Scanning (ALS) observations, which are costly and best suited for local research. Spaceborne LiDAR acquisitions overcome the limited spatiotemporal coverage of airborne systems, as they can cover much wider geographical areas. However, they do not provide continuous geographical data, requiring spatial interpolation methods to obtain wall-to-wall information. We developed a two-step, easily replicable methodology to estimate forest canopy fuel parameters for the entire European territory, based on data from the Global Ecosystem Dynamics Investigation (GEDI) sensor, onboard the International Space Station (ISS). First, we simulated GEDI pseudo-waveforms from discrete ALS data about forest plots. We then used metrics derived from the GEDI pseudo-waveforms to estimate mean canopy height (Hm), canopy cover (CC) and canopy base height (CBH), for which we used national forest inventory data as reference. The RH80 metric had the strongest correlation with Hm for all fuel types (r = 0.96?0.97, Bias = ?0.16-0.30 m, RMSE = 1.53?2.52 m, rRMSE = 13.23?19.75%). A strong correlation was also observed between ALS-CC and GEDI-CC (r = 0.94, Bias = ?0.02, RMSE = 0.09, rRMSE = 16.26%), whereas weaker correlations were obtained for CBH estimations based on forest inventory data (r = 0.46, Bias = 0 m, RMSE = 0.89 m, rRMSE = 39.80%). The second stage was to generate wall-to-wall maps for the continent of Europe of canopy fuel parameters at a resolution of 1 km using a spatial interpolation of GEDI-based estimates for within-fuel polygons covered by GEDI footprints. GEDI observations were not available for some of the polygons (mainly Northern latitudes, above 51.6°N). In these cases, the parameters were estimated using random forest regression models based on multispectral and SAR imagery and biophysical variables. Errors were higher than from direct GEDI retrievals, but still within the range of previous results (r = 0.72?0.82, Bias = ?0.18-0.29 m, RMSE = 3.63?4.18 m and rRMSE = 28.43?30.66% for Hm; r = 0.82?0.91, Bias = 0, RMSE = 0.07?0.09 and rRMSE = 10.65?14.42% for CC; r = 0.62?0.75, Bias = 0.01?0.02 m, RMSE = 0.60?0.74 m and rRMSE = 19.16?22.93% for CBH). Uncertainty maps for the estimated parameters were provided at the grid level, for which purpose we considered the propagation of individual errors for each step in the methodology. The final outputs, which are publicly available (https://doi.org/10.21950/KTALA8), provide a wall-to-wall estimation for the continent of Europe of three critical parameters for modelling crown fire propagation potential and demonstrate the capacity of GEDI observations to improve the characterisation of fuel models.Ministerio de Ciencia, Innovación y UniversidadesUniversidad de AlcaláEuropean Commissio

Utility of Sea Surface Height anomaly (SSHa)in determination of Potential Fishing Zones

Physical processes in the oceans can be monitored by altimeters well before a radiometer can in terms of temperature or chlorophyll concentration. Herein we show the importance of Sea Surface Height anomaly (SSHa, retrieved with altimeter) in demarcating potential fishing zones. We also show how SSHa can help predict tuna movements, horizontally as well as vertically in the water column. Moreover, we prove these prediction with positively correlating SSHa to tuna hooking rates. In the end, we list out present and potential future sources from where SSHa can be retrieved in order to provide improved fishery advisories

Biofuel scenarios in a water perspective: the global blue and green water footprint of road transport in 2030

The trend towards substitution of conventional transport fuels by biofuels requires additional water. The EU aims In the last two centuries, fossil fuels have been our major source of energy. However, issues concerning energy security and the quality of the environment have given an impulse to the development of alternative, renewable fuels. Particularly the transport sector is expected to steadily switch from fossil fuels to a larger fraction of biofuels - liquid transport fuels derived from biomass. Many governments believe that biofuels can replace substantial volumes of crude oil and that they will play a key role in diversifying the sources of energy supply in the coming decades. The growth of biomass requires water, a scarce resource. The link between water resources and (future) biofuel consumption, however, has not been analyzed in great detail yet. Existing scenarios on the use of water resources usually only consider the changes in food and livestock production, industry and domestic activity. The aim of this research is to assess the change in water use related to the expected increase in the use of biofuels for road transport in 2030, and subsequently evaluate the contribution to potential water scarcity. The study builds on earlier research on the relation between energy and water and uses the water footprint (WF) methodology to investigate the change in water demand related to a transition to biofuels in road transport. Information about this transition in each country is based on a compilation of different energy scenarios. The study distinguishes between two different bio-energy carriers, bio-ethanol and biodiesel, and assesses the ratio of fuel produced from selected first-generation energy crops per country. For ethanol these crops are sugar cane, sugar beet, sweet sorghum, wheat and maize. For biodiesel they are soybean, rapeseed, jatropha, and oil palm